Enhancing Failure Propagation Analysis in Cloud Computing Systems

In order to plan for failure recovery, the designers of cloud systems need to understand how their system can potentially fail. Unfortunately, analyzing the failure behavior of such systems can be very difficult and time-consuming, due to the large volume of events, non-determinism, and reuse of third-party components. To address these issues, we propose a novel approach that joins fault injection with anomaly detection to identify the symptoms of failures. We evaluated the proposed approach in the context of the OpenStack cloud computing platform. We show that our model can significantly improve the accuracy of failure analysis in terms of false positives and negatives, with a low computational cost.Comment: 12 pages, The 30th International Symposium on Software Reliability Engineering (ISSRE 2019

DCR: Double Component Ranking for Building Reliable Cloud Applications

Since cloud applications are usually large-scale, it is too expensive to enhance the reliability of all components for building highly reliable cloud applications. Therefore, we need to identify significant components which have great impact on the system reliability. FTCloud, an existing approach, ranks the components only considering the impact of component internal failures and ignoring error propagation. However, error propagation is also an important factor on the system reliability. To attack the problem, we propose an improved component ranking framework, named DCR, to identify significant components in cloud applications. DCR employs two individual algorithms to rank the components twice and determines a set of the most significant components based on the two ranking results. In addition, DCR does not require information of component invocation frequencies. Extensive experiments are provided to evaluate DCR and compare it with FTCloud. The experimental results show that DCR outperforms FTCloud in almost all cases

DFCV: A Novel Approach for Message Dissemination in Connected Vehicles using Dynamic Fog

Vehicular Ad-hoc Network (VANET) has emerged as a promising solution for enhancing road safety. Routing of messages in VANET is challenging due to packet delays arising from high mobility of vehicles, frequently changing topology, and high density of vehicles, leading to frequent route breakages and packet losses. Previous researchers have used either mobility in vehicular fog computing or cloud computing to solve the routing issue, but they suffer from large packet delays and frequent packet losses. We propose Dynamic Fog for Connected Vehicles (DFCV), a fog computing based scheme which dynamically creates, increments and destroys fog nodes depending on the communication needs. The novelty of DFCV lies in providing lower delays and guaranteed message delivery at high vehicular densities. Simulations were conducted using hybrid simulation consisting of ns-2, SUMO, and Cloudsim. Results show that DFCV ensures efficient resource utilization, lower packet delays and losses at high vehicle densities